Biomedical subjects

Kathy L Moser

Publications and source records attributed to Kathy L Moser.

15 recordsLinked to original sources

PubMed2006

Fine-mapping chromosome 20 in 230 systemic lupus erythematosus sib pair and multiplex families: evidence for genetic epistasis with chromosome 16q12.

The presence of systemic lupus erythematosus (SLE) susceptibility genes on chromosome 20 is suggested by the observation of genetic linkage in several independent SLE family collections. To further localize the genetic effects, we typed 59 microsatellites in the two best regions, as defined by genome screens. Genotypes were analyzed for statistical linkage and/or association with SLE, by use of a combination of nonparametric linkage methods, family-based tests of association (transmission/disequilibrium and pedigree disequilibrium tests), and haplotype-sharing statistics (haplotype runs test), in a set of 230 SLE pedigrees. Maximal evidence for linkage to SLE was to 20p12 (LOD = 2.84) and 20q13.1 (LOD = 1.64) in the white pedigrees. Subsetting families on the basis of evidence for linkage to 16q12 significantly improved the LOD scores at both chromosome 20 locations (20p12 LOD = 5.06 and 20q13 LOD = 3.65), consistent with epistasis. We then typed 162 single-nucleotide polymorphism markers across a 1.3-Mb candidate region on 20q13.1 and identified several SNPs that demonstrated significant evidence for association. These data provide additional support for linkage and association to 20p12 and 20q13.1 in SLE and further refine the intervals of interest. These data further suggest the possibility of epistatic relationships among loci within the 20q12, 20q13, and 16q12 regions in SLE families.

Chromosomes, Human, Pair 16↗

PubMed2006

Gene expression profiling in human autoimmunity.

Human autoimmune diseases are well suited for the application of gene expression profiling. Sampling of blood cells and target tissues has already revealed many important pathways contributing to this spectrum of disorders, and many commonalities are emerging. For instance, clinically distinct diseases such as systemic lupus erythematosus, Sjögren's syndrome, dermatomyositis, and psoriasis all show evidence for dysregulation of the type I interferon pathway. These data suggest that autoimmune diseases will eventually be categorized at the level of gene expression. This work has led to advances in our understanding of disease pathogenesis and in the future promises to facilitate assessments of disease activity and improve targeting of therapies. Here, we review the literature on gene profiling in human autoimmune diseases and provide perspective on the current state of the art.

Autoimmune Diseases↗

PubMed2005

Analysis of families in the multiple autoimmune disease genetics consortium (MADGC) collection: the PTPN22 620W allele associates with multiple autoimmune phenotypes.

Autoimmune disorders constitute a diverse group of phenotypes with overlapping features and a tendency toward familial aggregation. It is likely that common underlying genes are involved in these disorders. Until very recently, no specific alleles--aside from a few common human leukocyte antigen class II genes--had been identified that clearly associate with multiple different autoimmune diseases. In this study, we describe a unique collection of 265 multiplex families assembled by the Multiple Autoimmune Disease Genetics Consortium (MADGC). At least two of nine "core" autoimmune diseases are present in each of these families. These core diseases include rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), type 1 diabetes (T1D), multiple sclerosis (MS), autoimmune thyroid disease (Hashimoto thyroiditis or Graves disease), juvenile RA, inflammatory bowel disease (Crohn disease or ulcerative colitis), psoriasis, and primary Sjogren syndrome. We report that a recently described functional single-nucleotide polymorphism (rs2476601, encoding R620W) in the intracellular tyrosine phosphatase (PTPN22) confers risk of four separate autoimmune phenotypes in these families: T1D, RA, SLE, and Hashimoto thyroiditis. MS did not show association with the PTPN22 risk allele. These findings suggest a common underlying etiologic pathway for some, but not all, autoimmune disorders, and they suggest that MS may have a pathogenesis that is distinct from RA, SLE, and T1D. DNA and clinical data for the MADGC families are available to the scientific community; these data will provide a valuable resource for the dissection of the complex genetic factors that underlie the various autoimmune phenotypes.

Autoimmune Diseases↗

PubMed2005

Progress towards understanding the genetic pathogenesis of systemic lupus erythematosus.

In order to better understand the genetic factors that initiate systemic lupus erythematosus (SLE), we are using both linkage and association approaches to identify susceptibility genes for the disease. Association studies have recently identified three HLA Class II haplotypes as well as a functional missense polymorphism in protein tyrosine phosphatase (PTP) PTPN22 as important risk alleles for SLE. Here, we will review these data, and explain how these findings contribute to an understanding of the genetic architecture of human SLE.

Alleles↗

PubMed2005

A robust hybrid between genetic algorithm and support vector machine for extracting an optimal feature gene subset.

Development of a robust and efficient approach for extracting useful information from microarray data continues to be a significant and challenging task. Microarray data are characterized by a high dimension, high signal-to-noise ratio, and high correlations between genes, but with a relatively small sample size. Current methods for dimensional reduction can further be improved for the scenario of the presence of a single (or a few) high influential gene(s) in which its effect in the feature subset would prohibit inclusion of other important genes. We have formalized a robust gene selection approach based on a hybrid between genetic algorithm and support vector machine. The major goal of this hybridization was to exploit fully their respective merits (e.g., robustness to the size of solution space and capability of handling a very large dimension of feature genes) for identification of key feature genes (or molecular signatures) for a complex biological phenotype. We have applied the approach to the microarray data of diffuse large B cell lymphoma to demonstrate its behaviors and properties for mining the high-dimension data of genome-wide gene expression profiles. The resulting classifier(s) (the optimal gene subset(s)) has achieved the highest accuracy (99%) for prediction of independent microarray samples in comparisons with marginal filters and a hybrid between genetic algorithm and K nearest neighbors.

Algorithms↗

PubMed2004

Genetic association of the R620W polymorphism of protein tyrosine phosphatase PTPN22 with human SLE.

We genotyped 525 independent North American white individuals with systemic lupus erythematosus (SLE) for the PTPN22 R620W polymorphism and compared the results with data generated from 1,961 white control individuals. The R620W SNP was associated with SLE (genotypic P=.00009), with estimated minor (T) allele frequencies of 12.67% in SLE cases and 8.64% in controls. A single copy of the T allele (W620) increases risk of SLE (odds ratio [OR]=1.37; 95% confidence interval [CI] 1.07-1.75), and two copies of the allele more than double this risk (OR=4.37; 95% CI 1.98-9.65). Together with recent evidence showing association of this SNP with type 1 diabetes and rheumatoid arthritis, these data provide compelling evidence that PTPN22 plays a fundamental role in regulating the immune system and the development of autoimmunity.

Alleles↗

PubMed2004

The use of microarrays to study autoimmunity.

As with the development of any novel and potentially powerful technology, the prospect of revealing new information that may dramatically change our understanding of biological processes can generate much excitement. Such is true for the emerging genomic approaches that make possible high-density assays using microarray platforms. Indeed, it is difficult, if not impossible, to imagine any area of biology that could not be affected by the wide range of potential applications of microarray technology. Numerous examples, such as those from the field of oncology, provide striking evidence of the power of microarrays to bring about extraordinary advances in molecularly defining important disease phenotypes that were otherwise unrecognized using conventional approaches such as histology. However, only a few studies in autoimmunity are available to date. Very recent work in alopecia areata, multiple sclerosis, systemic lupus erythematosus, and Sjögren's syndrome illustrates the potential for gaining new insights into the pathophysiology of these complex autoimmune disorders on a global, molecular scale. These new insights are likely to significantly improve our understanding of disease processes, diagnosis, identification of new therapeutic targets, and identification of patients most likely to benefit from specific and tailored therapies.

Autoimmune Diseases↗

PubMed2004

An ensemble method for gene discovery based on DNA microarray data.

The advent of DNA microarray technology has offered the promise of casting new insights onto deciphering secrets of life by monitoring activities of thousands of genes simultaneously. Current analyses of microarray data focus on precise classification of biological types, for example, tumor versus normal tissues. A further scientific challenging task is to extract disease-relevant genes from the bewildering amounts of raw data, which is one of the most critical themes in the post-genomic era, but it is generally ignored due to lack of an efficient approach. In this paper, we present a novel ensemble method for gene extraction that can be tailored to fulfill multiple biological tasks including (i) precise classification of biological types; (ii) disease gene mining; and (iii) target-driven gene networking. We also give a numerical application for (i) and (ii) using a public microarrary data set and set aside a separate paper to address (iii).

Algorithms↗

PubMed2004

Keys to unlocking the mysteries of rheumatic autoimmune disease.

Autoimmune diseases are thought to affect between 14 million and 22 million people in this country. Despite decades of research, the underlying mechanisms of disease are poorly understood, diagnosis is often difficult, and therapies that minimize systemic side effects are lacking. Major advances in our understanding of human genetic variation and remarkable new technologies are paving the way for dramatically improving our fundamental knowledge of autoimmune diseases. Gene mapping studies have clearly illustrated the complexity of these diseases, which appear to involve many genes. Very high-throughput microarray assays that can measure the expression levels of thousands of genes simultaneously are revealing important insights into key biological pathways that appear to be perturbed in autoimmune diseases. We review recent advances in genetic and genomic studies, focusing primarily on systemic lupus erythematosus and related rheumatic autoimmune diseases such as Sjögren's syndrome and rheumatoid arthritis. Identification of susceptibility genes and dysregulated biological pathways for these diseases is likely to foster development of novel diagnostic and therapeutic approaches that are increasingly tailored to the underlying pathological mechanisms.

Alleles↗

PubMed2003

Genetic linkage analysis of longitudinal hypertension phenotypes using three summary measures.

BACKGROUND: Longitudinal data often have multiple (repeated) measures recorded along a time trajectory. For example, the two cohorts from the Framingham Heart Study (GAW13 Problem 1) contain 21 and 5 repeated measures for hypertension phenotypes as well as epidemiological risk factors, respectively. Direct modelling of a large number of serially and biologically correlated traits in the context of linkage analysis can be prohibitively complex. Alternatively, we may consider using univariate transformation for linkage analysis of longitudinal repeated measures. RESULTS: We evaluated the utility of three conventional summary measures (mean, slope, and principal components) for genetic linkage analysis of longitudinal phenotypes by analyzing the chromosome 10 data of the Framingham Heart Study. Except for the temporal slope, all of the summary methods and the multivariate analysis identified the previously reported region, marker GATA64A09, for systolic blood pressure or high blood pressure. Further analysis revealed that this region may harbor gene(s) affecting human blood pressure at multiple stages of life. CONCLUSION: We conclude that mean and principal components are feasible alternatives for genetic linkage analysis of longitudinal phenotypes, but the slope might have a separate genetic basis from that of the original longitudinal phenotypes.

Adult Children↗

PubMed2003

Multivariate sib-pair linkage analysis of longitudinal phenotypes by three step-wise analysis approaches.

BACKGROUND: Current statistical methods for sib-pair linkage analysis of complex diseases include linear models, generalized linear models, and novel data mining techniques. The purpose of this study was to further investigate the utility and properties of a novel pattern recognition technique (step-wise discriminant analysis) using the chromosome 10 linkage data from the Framingham Heart Study and by comparing it with step-wise logistic regression and linear regression. RESULTS: The three step-wise approaches were compared in terms of statistical significance and gene localization. Step-wise discriminant linkage analysis approach performed best; next was step-wise logistic regression; and step-wise linear regression was the least efficient because it ignored the categorical nature of disease phenotypes. Nevertheless, all three methods successfully identified the previously reported chromosomal region linked to human hypertension, marker GATA64A09. We also explored the possibility of using the discriminant analysis to detect gene x gene and gene x environment interactions. There was evidence to suggest the existence of gene x environment interactions between markers GATA64A09 or GATA115E01 and hypertension treatment and gene x gene interactions between markers GATA64A09 and GATA115E01. Finally, we answered the theoretical question "Is a trichotomous phenotype more efficient than a binary?" Unlike logistic regression, discriminant sib-pair linkage analysis might have more power to detect linkage to a binary phenotype than a trichotomous one. CONCLUSION: We confirmed our previous speculation that step-wise discriminant analysis is useful for genetic mapping of complex diseases. This analysis also supported the possibility of the pattern recognition technique for investigating gene x gene or gene x environment interactions.

Adult Children↗

PubMed2002

Visualizing human leukocyte antigen class II risk haplotypes in human systemic lupus erythematosus.

Human leukocyte antigen (HLA) class I and class II alleles are implicated as genetic risk factors for many autoimmune diseases. However, the role of the HLA loci in human systemic lupus erythematosus (SLE) remains unclear. Using a dense map of polymorphic microsatellites across the HLA region in a large collection of families with SLE, we identified three distinct haplotypes that encompassed the class II region and exhibited transmission distortion. DRB1 and DQB1 typing of founders showed that the three haplotypes contained DRB1*1501/ DQB1*0602, DRB1*0801/ DQB1*0402, and DRB1*0301/DQB1*0201 alleles, respectively. By visualizing ancestral recombinants, we narrowed the disease-associated haplotypes containing DRB1*1501 and DRB1*0801 to an approximately 500-kb region. We conclude that HLA class II haplotypes containing DRB1 and DQB1 alleles are strong risk factors for human SLE.

Alleles↗

PubMed2002

Increased prevalence of renal disease in systemic lupus erythematosus families with affected male relatives.

OBJECTIVE: To distinguish familial differences from sex-related differences in the clinical manifestations of systemic lupus erythematosus (SLE). METHODS: A total of 372 affected individuals from 160 multiplex SLE pedigrees were analyzed. Twenty-five of these pedigrees contained at least 1 affected male relative. Comparisons of the presence of each of the 11 1982 American College of Rheumatology criteria for SLE were made between female family members with affected male relatives and those without affected male relatives, using Fisher's exact tests. RESULTS: The presence of renal disease was significantly increased in female family members with an affected male relative when compared with those with no affected male relative (68% and 43%, respectively; P = 0.002). This trend remained after stratifying by race and was most pronounced in European Americans. A familial basis for differences in hematologic and immunologic manifestations was also suggested, while arthritis and dermatologic features appeared to be most influenced by sex. CONCLUSION: Our results demonstrate that the increased prevalence of renal disease previously reported in men with SLE is, in large part, a familial rather than sex-based difference, at least in multiplex SLE families. Distinguishing familial from sex-related differences may facilitate efforts to understand the genetic and hormonal factors that underlie this complex autoimmune disease.

Adult↗

PubMed2002

Genetic linkage and association of Fcgamma receptor IIIA (CD16A) on chromosome 1q23 with human systemic lupus erythematosus.

OBJECTIVE: Although low-affinity alleles of human Fcgamma receptor types IIA and IIIA (FcgammaRIIA and FcgammaRIIIA, respectively) polymorphisms have been associated with systemic lupus erythematosus (SLE) in case-control studies, the relative contribution of these genes to SLE susceptibility has not been resolved. METHODS: We analyzed the distribution of alleles of FcgammaRIIA, FcgammaRIIIA, and FcgammaRIIIB in 126 multiplex-SLE pedigrees and FcgammaRIIA and FcgammaRIIIA in a case-control replication study, using allele-specific polymerase chain reaction and direct sequencing of genomic DNA. Statistical tests of association were performed to detect evidence of linkage between the single nucleotide polymorphisms and SLE. RESULTS: We found evidence for linkage at both the FcgammaRIIIA (single-point nonparametric linkage [NPL] 1.8, P = 0.038; multipoint NPL 2.7, P = 0.004) and the FcgammaRIIA (single-point NPL 2.0, P = 0.021; multipoint NPL 2.6, P = 0.006) loci, but not the FcgammaRIIIB locus. Family-based tests of association demonstrated increased transmission of the low-affinity F176 allele at the FcgammaRIIIA locus (odds ratio [OR] 2.18, P = 0.0005 by transmission disequilibrium test and P = 0.002, by pedigree disequilibrium test [PDT]), but little evidence of preferential transmission of alleles at FcgammaRIIA (P = 0.089 by PDT). Stratification by ethnicity showed preferential transmission of the associated FcgammaRIIIA allele both in families of African American ancestry and in those of European American ancestry. Despite significant linkage disequilibrium between these genes, 2- and 3-locus haplotype analysis of the extended Fcgamma receptor cluster did not reveal any significant association beyond that observed with FcgammaRIIIA alone. In a large case-control replication study of 438 patients with SLE and 219 controls, FcgammaRIIIA provided the strongest evidence of an FcgammaR-SLE association (additive model: V/V 176 versus V/F 176 OR 1.51, V/V 176 versus F/F 176 OR 1.98, P = 0.007). CONCLUSION: To our knowledge, these data are the first to demonstrate linkage and both family-based and case-control-based association of FcgammaRIIIA with SLE. These data provide genetic evidence supporting a role for the physiologically relevant single nucleotide polymorphism of the FcgammaRIIIA gene in the pathophysiology of this complex genetic disease.

Case-Control Studies↗

PubMed2002

Recent advances in the genetics of systemic lupus erythematosus.

In recent years, we have witnessed an explosion in our understanding of the biology of SLE through the study of lupus-prone mice and the identification and subsequent narrowing of the genomic intervals likely responsible for SLE in human beings. The data from these efforts support the hypothesis that multiple genes contribute to disease susceptibility. Clearly, there is no single locus operating in all families multiplex for SLE, and the degree of ethnic and genetic heterogeneity seems to be quite significant. In this respect, the genetics of SLE resemble those of many other complex genetic diseases. The list of candidate genes and pathways (see Table 1) implicated in the pathogenesis of SLE is expanding at a rapid rate. Understanding how alterations in these genes and pathways lead to the lupus phenotype is the primary objective of future genetic studies in SLE.

Animals↗